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Assemble an editorial-motion podcast-clip ad from a config — a real clipped podcast MP3 carries the narrative while N flat limited-palette editorial-illustration keyframes (one look pack) are animated NOT by generative i2v but by DETERMINISTIC ffmpeg ken-burns (zoompan) + hard cuts (no crossfades, which expose geometric drift), each beat snapped to its spoken line, the real audio muxed, Whisper-driven captions burned only mid-sentence, and closed on a PIL brand end card — never AI-rendered text. This is the FREE deterministic assembly stage (ffmpeg ken-burns + hard concat + audio mux + captions + end card); the real audio is clipped from source and the keyframes come from create-image-fal. Use for the editorial-motion-podcast format.

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Assemble an animated character-explainer video ad (any art style — Pixar-style 3D, claymation, anime, paper-cut, felt) (~77s, 9:16) built on an "N types of X" listicle spine — a recurring human protagonist plus a locked cast of N persona characters, one per list item. Given the per-scene i2v clips + a per-scene target-duration table + a narration track, it trims each clip to its scene window, re-encodes every segment to identical 1080x1920/30fps/libx264/yuv420p (decrease+pad, never crop) so the concat demuxer never drops frames, concats, and muxes audio — in RESTYLE mode the source ad's VO+music mix is reused verbatim, in ORIGINAL mode fresh per-scene VO (loudnorm I=-14) is mixed under an optional music bed (loudnorm I=-26). A static-still fallback loops a scene's keyframe when its clip is missing/failed, so the master always assembles; libass captions are burned last. FREE deterministic assembly (Python + ffmpeg, no bash, no paid calls); the recipe supplies the clips, keyframes, VO or source audio, and caption table and gates the paid cast-anchor/keyframe/Kling-i2v/VO/music calls to their own capabilities. Use for the 3d-character-explainer listicle format.

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Reconstruct ancestral states at internal phylogenetic nodes for sequences (PAML codeml, IQ-TREE --ancestral, GRASP, FastML), discrete traits (corHMM hidden-rate Markov, ape::ace, phytools::make.simmap stochastic mapping, BayesTraits), and continuous traits (phytools::fastAnc, geiger Brownian/OU, RPANDA). Use when designing constructs for ancestral protein resurrection, tracing trait evolution along a tree, performing stochastic character mapping, testing models of trait evolution (BM vs OU vs EB), inferring ancestral genome content via Dollo or DTL reconciliation, or quantifying ancestral-state uncertainty for downstream comparative analyses.

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Build whole-genome alignments using Progressive Cactus (Armstrong 2020 reference-free clade-level WGA), Minigraph-Cactus (Hickey 2024 pangenome-aware), LASTZ chain/net (UCSC pipeline), MUMmer4 (Marçais 2018 pairwise), minimap2 -x asm5/10/20 (Li 2018 fast pairwise), AnchorWave (Song 2022 WGD-aware), and Mauve / progressiveMauve (bacterial). Operates the HAL toolkit (Hickey 2013) for downstream extraction including halSynteny, halLiftover, halBranchMutations, and hal2maf. Use when constructing multi-species alignments for comparative-annotation projection (TOGA), synteny detection, conservation analyses (phyloP / PhastCons), or pangenome graph construction; selecting between reference-free (Cactus) and reference-anchored (LASTZ chains/nets) approaches; tuning sensitivity for closely vs distantly related genomes; or producing HAL files for genome-wide downstream tools.

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Use this skill when conditioning, loading, preparing, or labeling signal data for analysis or ML training. Covers: cleaning a single signal (fill gaps, remove drift, deoutlier, denoise, resample/align a time base) BEFORE analysis; building a `signalDatastore` pipeline; creating a `labeledSignalSet` for Signal Labeler; deriving labels (filename, folder, in-file, ROI, time-frequency ROI); stratified train/val/test splits; framing long signals; parallel processing; and shaping datastore output for `trainnet`. Triggers include "clean up this signal", "remove drift / detrend", "fill gaps", "remove spikes / outliers", "denoise", "resample to a uniform rate", "align channels", "labels from filenames", "stratified split", "prepare for Signal Labeler", and function names like `fillgaps`, `fillmissing`, `detrend`, `filloutliers`, `smoothdata`, `resample`, `synchronize`, `signalDatastore`, `labeledSignalSet`, `filenames2labels`, `folders2labels`, `splitlabels`, `framesig`, `framelbl`, `createDatastores`.

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Build MCP servers in Python with FastMCP. Define tools / resources / prompts, build the server, test locally, deploy to FastMCP Cloud or Docker. Use whenever the user mentions building an MCP server, exposing tools to LLMs, FastMCP, building a Claude integration, or troubleshooting FastMCP module-level server, storage, lifespan, middleware, OAuth, or deployment errors.

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Cross-framework infrastructure security audit across cloud, network, and CI/CD. Use for infrastructure and cloud security audits, security posture assessment, and validating technical controls for SOC 2, ISO 27001, and NIST CSF.

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Use when the user asks to "design cloud architecture", "set up Terraform infrastructure", "optimize cloud costs", "plan disaster recovery", "configure multi-region failover", "audit IAM policies", "migrate to cloud", "set up VPC networking", "compare AWS vs GCP vs Azure", or "right-size cloud instances". Expert cloud architecture covering AWS, GCP, Azure, multi-cloud strategy, cost optimization, security, and infrastructure automation.

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Expert knowledge for Azure Bastion development including best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, and integrations & coding patterns. Use when configuring Bastion for AKS private clusters, VM scale sets, Entra ID auth, hub/spoke VNets, or IP-based cross-VNet access, and other Azure Bastion related development tasks. Not for Azure Virtual Network (use azure-virtual-network), Azure Virtual Network Manager (use azure-virtual-network-manager), Azure VPN Gateway (use azure-vpn-gateway), Azure Firewall (use azure-firewall).

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Build a WhatsApp marketing strategy — WhatsApp Business setup, broadcast campaigns, automated flows, customer service, drip sequences, and conversational marketing. Use when the user says "WhatsApp marketing", "WhatsApp Business", "WhatsApp campaigns", "WhatsApp broadcasts", "WhatsApp automation", "WhatsApp drip", "market on WhatsApp", "WhatsApp for business", "WhatsApp chatbot", "WhatsApp customer service", "WhatsApp API", "messaging marketing India", or wants to use WhatsApp as a marketing and customer communication channel. Especially relevant for brands in India, the Middle East, Southeast Asia, Brazil, and Africa.

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Use when a PERSONAL brand needs AUDIO — voice cloning with ElevenLabs, Murf, or PlayHT, podcast production, audiobooks, and voiceover: short voiceover for TikTok and Reels, a 30 to 60 minute podcast format, and a 1-to-10 repurpose turning one episode into ten clips, in English with US, UK, AU, and SG accents. Trigger on 'voice clone', 'ElevenLabs', 'start a podcast', 'audiobook narration', 'AI voiceover for my videos', 'I hate re-recording the same intro'. Not for — video with a talking-head avatar, see `24-ai-avatar-production-global`; the script being read aloud, see `04-script-video-global`; written long-form posts, see `26-thought-leadership-content-global`.

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Customise the prd-taskmaster plugin workflow via curated brainstorm questions. The AI asks, the user answers in plain English, and the skill writes their preferences to .atlas-ai/config/atlas.json. Future runs of prd-taskmaster read that file and apply user preferences to phase gates, validation strictness, default provider, preferred execution mode, and template choice. For deeper tweaks beyond the curated questions, users can hand-edit files in .atlas-ai/customizations/. Use when the user says "customise workflow", "customize workflow", "adjust my PRD settings", "tune the skill", or wants to change how prd-taskmaster behaves.

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Phase 2 of the prd-taskmaster pipeline: spec generation and task parsing. Loads a template (comprehensive|minimal), fills it with DISCOVER-phase constraints and answers, validates the spec (placeholders_found, grade thresholds), parses the PRD into tasks via task-master, runs TaskMaster's native complexity analysis, and expands every task into verifiable subtasks. Autonomous-safe. Declares GENERATE complete so HANDOFF can follow.

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Managing cloud infrastructure using declarative and imperative IaC tools. Use when provisioning cloud resources (Terraform/OpenTofu for multi-cloud, Pulumi for developer-centric workflows, AWS CDK for AWS-native infrastructure), designing reusable modules, implementing state management patterns, or establishing infrastructure deployment workflows.

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Detects duplicate boilerplate, copy-paste tests, and structural maintainability issues across .NET test suites. Use when the user asks to reduce repetition, consolidate similar test methods, convert copy-paste tests to data-driven parameterized tests, suggest a better test structure, or identify refactoring opportunities. Identifies repeated construction, assertion patterns, copy-paste methods convertible to DataRow/Theory/TestCase, redundant setup/teardown, and shared infrastructure. Produces an analysis report with concrete before/after suggestions. Works with MSTest, xUnit, NUnit, and TUnit. DO NOT USE FOR: writing new tests (use writing-mstest-tests), reviewing test quality or anti-patterns (use test-anti-patterns), or deep mock auditing (use exp-mock-usage-analysis).

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Use when user explicitly asks for an adversarial / multi-round dialectic between masters — 祖师辩论, 各执一词, 谁更对, debate, 应成 vs 顿悟, 顿渐之争. Differs from /compare-masters (parallel single-round) by being adversarial multi-round via fresh-subagent orchestration. Topics 空有 / 禅净 / 性相 / 戒律 vs 内观 — trigger is adversarial framing: "禅净比较" → compare; "禅净辩论 / 谁更究竟" → here.

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Use when user asks to compare masters, compare schools, compare perspectives, 对比, 各宗怎么看, 不同宗派, 禅净之争, 性相之辩, 空有之争, or wants multiple masters to answer the same question. Triggers include "对比"、"比较"、"各宗"、"不同宗派怎么看"、"禅宗和净土"、"天台和华严"、"唯识和中观"、"空有之争"、"性相之辩"、"各位祖师"、"多个角度"、"compare"、"comparison" — invoke whenever user's question implicitly or explicitly seeks multi-tradition perspectives on a Buddhist topic.

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Primary Python toolkit for molecular biology. Preferred for Python-based PubMed/NCBI queries (Bio.Entrez), sequence manipulation, file parsing (FASTA, GenBank, FASTQ, PDB), advanced BLAST workflows, structures, phylogenetics. For quick BLAST, use gget. For direct REST API, use pubmed-database.

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All-in-one FASTQ QC and adapter trimming. Auto-detects Illumina adapters, filters low-quality reads, corrects paired-end overlaps, emits HTML+JSON QC in one pass. 3-10x faster than Trim Galore/Trimmomatic. First step before STAR, BWA-MEM2, or Salmon.

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Core Python library for astronomy/astrophysics: units with dimensional analysis, celestial coordinate transforms (ICRS/Galactic/AltAz/FK5), FITS I/O, tables (FITS/HDF5/VOTable/CSV), cosmology (Planck18, distance/age), precise time (UTC/TAI/TT/TDB, Julian, barycentric), WCS pixel-world mapping, model fitting. For general tables use pandas/polars; for radio interferometry use CASA.

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Transform podcast transcripts into comprehensive content marketing suites including blog posts, social media content, newsletters, SEO-optimized articles, and timestamps. Use when user provides podcast transcripts or wants to repurpose podcast content.

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Repository operating skill for using Flow Forecast's flood_forecast package for time-series forecasting, data preparation, training, inference, evaluation, and hydrology-oriented advanced models.

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Astropy is the core Python package for astronomy, providing essential functionality for astronomical research and data analysis.

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Transform podcast transcripts into comprehensive content marketing suites including blog posts, social media content, newsletters, SEO-optimized articles, and timestamps. Use when user provides podcast transcripts or wants to repurpose podcast content.

日本語の概要は準備中です。原文の説明を表示しています。